Development and evaluation of a prospective staffing model to improve retention

To improve predictability and accuracy of hiring using historical staffing data, quality improvement and workforce engagement.

Twenty-three per cent of newly licensed nurses leave their first job within one year, costing employers $52,100 per nurse replacement. Tools for anticipatory hiring strategies are not available in the literature.

We used retrospective, secondary data analysis to develop a Prospective Staffing Model and conduct a five-year longitudinal evaluation of the implementation of the model in a convenience sample at a quaternary academic Cardiothoracic Intensive Care Unit. We used a team-based, quality improvement approach to restructure recruitment and hiring strategies, standardize new graduate nurse orientation and implement AACN Healthy Work Environment standards.

Over the five-year prospective evaluation period (2014-2018), 388 nurses were hired and included in the evaluation cohort. Retention increased (n = 286 days) and turnover decreased (17.6%) between 2014 and 2018. Improvements in workforce stability were sustained at five years.

Use of a Prospective Staffing Model is associated with improved nurse retention and decreased turnover, and may improve workforce stability.

Results suggest that an innovative tool can mitigate the deleterious effects of turnover, adding to current knowledge and providing a method for anticipatory assessment of local turnover.